import argparse
import torch
import torch.nn.functional as F
import torch_geometric.transforms as T
from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
from torch_geometric.nn import GCNConv, SAGEConv

from .logger import Logger


class GCN(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, num_layers, dropout):
        super(GCN, self).__init__()

        self.convs = torch.nn.ModuleList()
        self.convs.append(GCNConv(in_channels, hidden_channels, cached=True))
        self.bns = torch.nn.ModuleList()
        self.bns.append(torch.nn.BatchNorm1d(hidden_channels))
        for _ in range(num_layers - 2):
            self.convs.append(GCNConv(hidden_channels, hidden_channels, cached=True))
            self.bns.append(torch.nn.BatchNorm1d(hidden_channels))
        self.convs.append(GCNConv(hidden_channels, out_channels, cached=True))

        self.dropout = dropout

    def reset_parameters(self):
        for conv in self.convs:
            conv.reset_parameters()
        for bn in self.bns:
            bn.reset_parameters()

    def forward(self, x, adj_t):
        for i, conv in enumerate(self.convs[:-1]):
            x = conv(x, adj_t)
            x = self.bns[i](x)
            x = F.relu(x)
            x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.convs[-1](x, adj_t)
        return x.log_softmax(dim=-1)


class SAGE(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, num_layers, dropout):
        super(SAGE, self).__init__()

        self.convs = torch.nn.ModuleList()
        self.convs.append(SAGEConv(in_channels, hidden_channels))
        self.bns = torch.nn.ModuleList()
        self.bns.append(torch.nn.BatchNorm1d(hidden_channels))
        for _ in range(num_layers - 2):
            self.convs.append(SAGEConv(hidden_channels, hidden_channels))
            self.bns.append(torch.nn.BatchNorm1d(hidden_channels))
        self.convs.append(SAGEConv(hidden_channels, out_channels))

        self.dropout = dropout

    def reset_parameters(self):
        for conv in self.convs:
            conv.reset_parameters()
        for bn in self.bns:
            bn.reset_parameters()

    def forward(self, x, adj_t):
        for i, conv in enumerate(self.convs[:-1]):
            x = conv(x, adj_t)
            x = self.bns[i](x)
            x = F.relu(x)
            x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.convs[-1](x, adj_t)
        return x.log_softmax(dim=-1)


def train(model, data, train_idx, optimizer):
    model.train()

    optimizer.zero_grad()
    out = model(data.x, data.adj_t)[train_idx]
    loss = F.nll_loss(out, data.y.squeeze(1)[train_idx])
    loss.backward()
    optimizer.step()

    return loss.item()


@torch.no_grad()
def test(model, data, split_idx, evaluator):
    model.eval()

    out = model(data.x, data.adj_t)
    y_pred = out.argmax(dim=-1, keepdim=True)

    train_acc = evaluator.eval(
        {
            "y_true": data.y[split_idx["train"]],
            "y_pred": y_pred[split_idx["train"]],
        }
    )["acc"]
    valid_acc = evaluator.eval(
        {
            "y_true": data.y[split_idx["valid"]],
            "y_pred": y_pred[split_idx["valid"]],
        }
    )["acc"]
    test_acc = evaluator.eval(
        {
            "y_true": data.y[split_idx["test"]],
            "y_pred": y_pred[split_idx["test"]],
        }
    )["acc"]

    return train_acc, valid_acc, test_acc


def main():
    parser = argparse.ArgumentParser(description="OGBN-Arxiv (GNN)")
    parser.add_argument("--device", type=int, default=0)
    parser.add_argument("--log_steps", type=int, default=1)
    parser.add_argument("--use_sage", action="store_true")
    parser.add_argument("--num_layers", type=int, default=3)
    parser.add_argument("--hidden_channels", type=int, default=256)
    parser.add_argument("--dropout", type=float, default=0.5)
    parser.add_argument("--lr", type=float, default=0.01)
    parser.add_argument("--epochs", type=int, default=500)
    parser.add_argument("--runs", type=int, default=10)
    args = parser.parse_args()
    print(args)

    device = f"cuda:{args.device}" if torch.cuda.is_available() else "cpu"
    device = torch.device(device)

    dataset = PygNodePropPredDataset(name="ogbn-arxiv", transform=T.ToSparseTensor())

    data = dataset[0]
    data.adj_t = data.adj_t.to_symmetric()
    data = data.to(device)

    split_idx = dataset.get_idx_split()
    train_idx = split_idx["train"].to(device)

    if args.use_sage:
        model = SAGE(
            data.num_features,
            args.hidden_channels,
            dataset.num_classes,
            args.num_layers,
            args.dropout,
        ).to(device)
    else:
        model = GCN(
            data.num_features,
            args.hidden_channels,
            dataset.num_classes,
            args.num_layers,
            args.dropout,
        ).to(device)

    evaluator = Evaluator(name="ogbn-arxiv")
    logger = Logger(args.runs, args)

    for run in range(args.runs):
        model.reset_parameters()
        optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
        for epoch in range(1, 1 + args.epochs):
            loss = train(model, data, train_idx, optimizer)
            result = test(model, data, split_idx, evaluator)
            logger.add_result(run, result)

            if epoch % args.log_steps == 0:
                train_acc, valid_acc, test_acc = result
                print(
                    f"Run: {run + 1:02d}, "
                    f"Epoch: {epoch:02d}, "
                    f"Loss: {loss:.4f}, "
                    f"Train: {100 * train_acc:.2f}%, "
                    f"Valid: {100 * valid_acc:.2f}% "
                    f"Test: {100 * test_acc:.2f}%"
                )

        logger.print_statistics(run)
    logger.print_statistics()


if __name__ == "__main__":
    main()
